Between 1980 and 1997, municipal waste in OECD countries increased by around 40%. This paper outlines the very real negative effects of this increase and then introduces the two main European Union policies that have been established to address this problem: a landfill directive and legislation on extended producer responsibility (EPR). The paper then describes and compares the four alternative strategies to reducing end-of-life waste within the context of extended producer responsibility: namely repairing, reconditioning, remanufacturing or recycling. It also introduces a more robust definition of remanufacturing, validated by earlier research, which differentiates it from repair and reconditioning. From a consideration of the different factors involved, it concludes that remanufacturing may well be the best strategy. This is because it enables the embodied energy of virgin production to be maintained, preserves the intrinsic 'added value' of the product for the manufacturer and enables the resultant products to be sold 'as new' with updated features if necessary. Copyright © 2005 John Wiley & Sons, Ltd and ERP Environment.

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Key manufacturing challenges include pollution, natural resource depletion, waste management and landfill space. Remanufacturing, a process of bringing used products to a 'like-new' functional state with warranty to match, is being regarded as a vital strategy in waste management and environmentally conscious manufacturing. There is a paucity of remanufacturing knowledge, particularly in design-for-remanufacturing (DFRem) because of its relative novelty in research terms. This paper outlines the elements of the remanufacturing concept, details existing design-for-remanufacturing research and describes findings from recent UK industrial case studies undertaken to verify and augment previous research. It proposes some fundamental steps required to build on past work to improve the robustness of DFRem methodologies

Deep learning has emerged as a state-of-the-art learning technique across a wide range of applications, including image recognition, object detection and localisation, natural language processing, prediction and forecasting systems. With significant applicability, deep learning could be used in new and broader areas of applications, including remanufacturing. Remanufacturing is a process of taking used products through disassembly, inspection, cleaning, reconditioning, reassembly and testing to ascertain that their condition meets new products conditions with warranty. This process is complex and requires a good understanding of the respective stages for proper analysis. Inspection is a critical process in remanufacturing, which guarantees the quality of the remanufactured products. It is currently an expensive manual operation in the remanufacturing process that depends on operator expertise, in most cases. This research investigates the application of deep learning algorithms to inspection in remanufacturing, towards automating the inspection process. This paper presents a novel vision-based inspection system based on deep convolution neural network (DCNN) for eight types of defects, namely pitting, rust, cracks and other combination faults. The materials used for this feasibility study were 100 cm × 150 cm mild steel plate material, purchased locally, and captured using a USB webcam of 0.3 megapixels. The performance of this preliminary study indicates that the DCNN can classify with up to 100% accuracy on validation data and above 96% accuracy on a live video feed, by using 80% of the sample dataset for training and the remaining 20% for testing. Therefore, in the remanufacturing parts inspection, the DCNN approach has high potential as a method that could surpass the current technologies used in the design of inspection systems. This research is the first to apply deep learning techniques in remanufacturing inspection. The proposed method offers the potential to eliminate expert judgement in inspection, save cost, increase throughput and improve precision. This preliminary study demonstrates that deep learning techniques have the potential to revolutionise inspection in remanufacturing. This research offers valuable insight into these opportunities, serving as a starting point for future applications of deep learning algorithms to remanufacturing.